An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation
In this paper, an innovative model of agent based simulation, based on Ant Colony Optimization (ACO) algorithm is proposed in order to compare three available strategies of clearing wholesale electricity markets, i.e. uniform, pay-as-bid, and generalized Vickrey rules. The supply side actors of the power market are modeled as adaptive agents who learn how to bid strategically to optimize their profit through indirect interaction with other actors of the market. The proposed model is proper for bidding functions with high number of dimensions and enables modelers to avoid curse of dimensionality as dimension grows. Test systems are then used to study the behavior of each pricing rule under different degrees of competition and heterogeneity. Finally, the pricing rules are comprehensively compared using different economic criteria such as average cleared price, efficiency of allocation, and price volatility. Also, principle component analysis (PCA) is used to rank and select the best price rule. To the knowledge of the authors, this is the first study that uses ACO for assessing strategies of wholesale electricity market.
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